Trading in the FX market using mechanical trading strategies

When using purely speculative instruments like Forex pairs to make money, it becomes clear that being in the market is a big disadvantage. The longer you remain inside the market, the longer price can change against you and the less power your signal has in predicting overall direction. The power of trading signals to predict outcomes will often decay exponentially after trade entry and it is therefore very important to be able to judge where a trade is going and make a decision whether you can to stay or leave a trade very quickly. In this sense, it seems ideal to be able to build trading systems for which market exposure is absolutely minimal, systems that will stay in the market for as little as possible to extract the same gain as systems that would stay in the market for much longer. On today’s post I want to talk about this type of systems and how we can easily find such strategies using data-mining techniques.

Let us suppose that we want to be able to extract an average of 100 pips on every winning trade. There are certainly many ways in which you can do this if you look at price charts. Each different system has a different average trade length but there is clearly a system for which the time in the market to attain this gain is minimal. You would not want to take a trade that needs to wait 15 days in order to achieve this gain but you would want to take the trade that gives you this gain in the minimum amount of time. While both systems might be exactly the same regarding all other aspects (number of trades, drawdown, profit, etc), the system that remains less time in the market has the advantage of being less exposed to unfavorable changes in market conditions. A system that opens up a trade and expects to make a profit in 5 hours will be much easier to trade than a system that need 5 days instead.

All things being equal, a system that remains less time in the market is the ideal choice, because its exposure is more limited and its signals are by nature more accurate. If both systems make 100 pips per trade, I would rather trade the system that has a faster exit than the system that has the slower exit, because with the larger exit I have more risk exposure (more time in the market). Note however that if a system makes 10 pips per trade with a faster exit and the other still makes 100 pips I would go with the larger pip gain per trade because the increase in exposure of the system that trades longer is compensated by its much larger exit. Choosing the system with the least market exposure only makes sense if all other things remain equal and you are gaining simply from a more accurate signal targeting.

But how do you build systems with “fast exits”? Data-mining gives us the ability to search for systems with this precise characteristic. By using stop-loss functions where the stop-loss changes as a function of time, we can set break-even points that are very close to the system’s entry signal. If the data-mining bias is calculated to be low enough, these systems are perfectly viable for live trading and often yield profits that are close to those of systems with a much larger exposure. The systems displayed within this post are daily systems using a 2 day break-even linear stop. This means that the SL changes linearly from an initial value of 100-200% of the ATR to break-even after two days and continues to move aggressively thereafter. This implies that the signals generated are highly accurate, because they are historically profitable even though their linear SL demands a very fast evolution of price action after trade entry.

Generally such accurate fast-exit systems are rather rare on the upper timeframes but they are much more common and profitable across lower time frames because they often take advantage of daily volatility cycles (market open/closes, etc). For example you can find systems with 2-4 hour exits on the EUR/USD with average yearly profit to maximum drawdown ratios near 1, because highly accurate signals related with volatility spikes across market opening/closing times can be found. These systems often have profitability similar to that of the daily systems shown in this post but they exit much faster because they are executed on the daily time frame. Such hourly systems are one of my main system data-mining targets as they offer a very significant potential edge while minimizing market exposure. There are also some interesting aspects regarding data-mining bias when searching for such systems, which I will leave for future posts.

If you would like to learn more about data-mining and how you too can mine for systems using adequate data-mining bias determination and fast GPU mining please consider joining Asirikuy.com, a website filled with educational videos, trading systems, development and a sound, honest and transparent approach towards automated trading in general . I hope you enjoyed this article ! :o)

“By using stop-loss functions where the stop-loss changes as a function of time, we can set break-even points that are very close to the system’s entry signal. If the data-mining bias is calculated to be low enough, these systems are perfectly viable for live trading and often yield profits that are close to those of systems with a much larger exposure. ”

But data mining bias cannot be calculate din principle because it is data dependent and the stop method you described leads to overfitting.

Thanks for posting :o). The stop method is also tested on the bootstrapped data (on at least 50 random data sets derived from the real data), if this method leads to any sort of increase in the probability to derive systems from randomness, it will come out here. I gain nothing from including an additional stop condition. Making the selection of the stop does not lead to curve-fitting, because I’m simply imposing it as a system requirement (what I deem as an acceptable system), note that searching for profitable systems that use a certain type of stop does not automatically lead to “more profitable systems” (more curve-fitted systems) it just leads to systems with fast exits. Making the stop selection is alike making any other type of selection (what system type, how many rules, what shifts, etc). The data-mining bias determination as described in my previous post, is independent of the search space selected because it always tests this search space against random data sets. The probability to find such systems in random data is independent of how I selected the search space. Thanks again for posting :o)